Method, system and computer program product for detecting movements of the vehicle body in the case of a motor vehicle

ABSTRACT

A method for detecting movements of a body ( 12 ) of a first motor vehicle ( 10 ) includes recording image and sensor data by a camera and sensor device ( 30 ) of a second motor vehicle ( 20 ). The image and sensor data represent that part of the environment of the second motor vehicle ( 20 ) that contains the first motor vehicle ( 10 ). The image and sensor data are forwarded to a data analysis device that detects movements of the body of the first motor vehicle and uses artificial intelligence algorithms and machine image analysis to process the image and sensor data to classify movements of the vehicle body ( 12 ). The classified movements of the vehicle body ( 12 ) are assigned to at least one of a set of defined states. Output data from the determined state are generated for further use in an automated driving function and/or for a user interface.

CROSS REFERENCE TO RELATED APPLICATION

This application claims priority under 35 USC 119 to German Patent Appl.No. 10 2020 130 886.8 filed on Nov. 23, 2020, the entire disclosure ofwhich is incorporated herein by reference.

BACKGROUND Field of the Invention

The invention relates to a method, a system and a computer programproduct for detecting movements of the vehicle body of a motor vehicle.

Related Art

If a motor vehicle is controlled by a person, the driver of the vehicleobserves very closely and intuitively whether there are any possiblesources of danger on the route. In particular, vehicles traveling infront are observed closely to obtain different information, for examplethe speed of the vehicle traveling in front or whether an overtakingmaneuver is planned. The driver also may observe whether the vehiclebody of the vehicle is moving in a straight line or whether fluctuatinglateral movements or up and down movements occur, such as those causedby bumps or obstacles on the road. A human driver intuitively carriesout these observations while driving and is often entirely aware of howhe processes the information, assigns it to a possible dangeroussituation and accordingly controls the vehicle.

Some vehicles can drive in a partially autonomous or autonomous manner,and these vehicles may use camera systems and sensors to obtaininformation relating to the environment of the vehicle. The developmentof highly automated driving is therefore associated with an increase inthe requirements imposed on vehicle sensor systems for recordingsuitable sensor data such as image data. In addition, the recordedsensor data must be interpreted carefully to obtain the correctconclusions with regard to a possible dangerous situation.

U.S. Pat. No. 11,046,312 describes a driving assistance device having aguide route specification unit, a driving environment informationdetermination unit, a target route specification unit, a control unitfor maintaining the vehicle distance, a calculation unit for an extentof lateral movement, an obstacle evasion process detection unit and afollowing controller to follow a vehicle traveling in front. Thecalculation unit for an extent of lateral movement calculates an extentof lateral movement of the vehicle traveling in front. The obstacleevasion process detection unit detects an obstacle evasion process ofthe vehicle traveling in front.

An object of this invention is to provide a reliable and efficientmethod, system and computer program product for detecting movements ofthe vehicle body of a motor vehicle.

SUMMARY

A first aspect of the invention relates to a method of using a dataanalysis device for detecting movements of the body of a motor vehicle.The method comprises:

-   -   recording image and sensor data by means of a camera and sensor        device of a second motor vehicle, wherein the image and sensor        data represent the environment of the second motor vehicle        containing at least the first motor vehicle;    -   forwarding the image and sensor data to the data analysis        device, where the data analysis device comprises a detection        system that uses algorithms from the field of artificial        intelligence (AI) and machine image analysis for detecting        movements of the vehicle body of the first motor vehicle;    -   processing the image and sensor data in the data analysis device        by means of the detection system to classify possible movements        of the vehicle body;    -   assigning the classified movements of the vehicle body to at        least one state S_(j) from a set of defined states S₁, S₂, . . .        , S_(n); and    -   generating output data from the determined state S_(j) for        further use in an automated driving function and/or for a user        interface.

The method may include processing image and sensor data in real time andoutputting data to be generated in real time.

In one embodiment, the detection system comprises an analysis module anda classification module.

The detection system advantageously uses deep learning with a neuralnetwork.

In particular, the neural network may be in the form of a convolutionalneural network.

One development provides for the classification module to containfeatures M₁, M₂, . . . , M_(n) of movements of a vehicle body of avehicle that were determined or predefined in a training phase of theclassification module.

The image and sensor data may be transmitted to the data analysis deviceby means of a mobile radio connection, and 5G radio modules may be used.

One embodiment provides for the image and sensor device to comprise anaction camera and/or acoustic sensors and/or a LiDAR system and/or anultrasonic system and/or a radar system.

A second aspect of the invention relates to a system for detectingmovements of the body of a motor vehicle. The system comprises an imageand sensor device of a second motor vehicle for recording image andsensor data, and a data analysis device. The image and sensor datarepresent the environment of the second motor vehicle containing atleast the first motor vehicle. The data analysis device comprises adetection system for detecting movements of the vehicle body the firstmotor vehicle. The detection system uses algorithms from the field ofartificial intelligence (AI) and machine image analysis. The dataanalysis device is designed to process the image and sensor data bymeans of the detection system, to classify possible movements of thevehicle body, to assign at least one state S_(j) from a set of definedstates S₁, S₂, . . . , S_(n) to the classified movements of the vehiclebody and to generate output data from the determined state S_(j) forfurther use in an automated driving function and/or for a userinterface.

The image and sensor data to be processed and for output data to begenerated in real time.

In one embodiment, the detection system comprises an analysis module anda classification module.

The detection system advantageously uses deep learning with a neuralnetwork, such as a convolutional neural network.

The classification module may contain features M₁, M₂, . . . , M_(n) ofmovements of the vehicle body of a motor vehicle that were determined orpredefined in a training phase of the classification module.

The image and sensor data may be transmitted to the data analysis deviceby a mobile radio connection that uses a 5G radio module in someembodiments.

The image and sensor device may comprise an action camera and/oracoustic sensors and/or a LiDAR system and/or an ultrasonic systemand/or a radar system.

The invention also relates to a computer program product comprising anexecutable program code that is configured such that, during itsexecution, the computer program product carries out the method describedherein.

The invention is explained in more detail below on the basis of anexemplary embodiment illustrated in the drawing.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a schematic illustration of a system according to theinvention for detecting movements of the vehicle body of a motorvehicle.

FIG. 2 schematically illustrates a detection system according to anembodiment of the invention.

FIG. 3 is a flowchart illustrating the steps of a method of theinvention.

FIG. 4 schematically illustrates a computer program product according toan embodiment of the invention.

DETAILED DESCRIPTION

FIG. 1 illustrates the system 100 according to the invention. A firstmotor vehicle 10 traveling in front has a vehicle body 12 and isobserved by a second motor vehicle 20 following the first motor vehicle10. The second motor vehicle 20 has a camera and sensor device 30 forrecording image and sensor data 32 in a recording area. The camera andsensor device 30 records the environment of the second motor vehicle 20in the recording area which is oriented to a road 22 that is in front ofthe second motor vehicle 20 and on which the first motor vehicle 10 issituated. The vehicle body 12 of the first motor vehicle 10 may move ina straight line or may fluctuate laterally or may move up and down, toindicate bumps or obstacles on the road. Furthermore, braking andacceleration processes may take place so that the vehicle body 12 movesat a changed speed. The camera and sensor device 30 forwards therecorded image and sensor data 32 to a data analysis device 40 forfurther processing.

The camera and sensor device 30 comprises an RGB camera 35 in thevisible range with the primary colors of blue, green and red. However, aUV camera in the ultraviolet range and/or an IR camera in the infraredrange may also be additionally provided. Cameras that differ in terms oftheir recording spectrum may therefore image different light conditionsin the recording area.

The recording frequency of the camera of the camera and sensor device 30is designed for fast speeds of the vehicle 10 traveling in front and canrecord image data 32 at a high image recording frequency. Furthermore,the camera and sensor device 30 may be equipped with acoustic sensors 37for capturing acoustic signals, such as a microphone. This makes itpossible to record rolling noises of the tires or engine noises.Furthermore, the image and sensor device 30 may have a LiDAR system, anultrasonic system and/or a radar system 39 to measure, for example, thedistance between the first motor vehicle 10 traveling in front and thefollowing second motor vehicle 20 or the speed of the first motorvehicle 10 traveling in front. In particular, this makes it possible tocapture information in three-dimensional space.

Provision may also be made for the camera and sensor device 30 to startthe image recording process automatically when there is a significantchange in size in the recording area of the camera and sensor device 30,for example, if a vehicle 10 appears in the recording area of the cameraand sensor device 30. This enables a selective data capture process, andonly relevant image and sensor data 32 are processed by the dataanalysis device 40. This makes it possible to use computing capacitiesmore efficiently.

In addition, a GPS connection (Global Positioning System) advantageouslyis provided to determine the and to assign the geographical location tothe recorded image and sensor data 32.

The camera 35 may be a weatherproof action camera arranged in the outerregion of the vehicle 20. Action cameras have wide-angle fisheye lenses,thus making it possible to achieve a visible radius of approximately180°. Action cameras can usually record videos in full HD (1920×1080pixels), but it is also possible to use action cameras in ultra HD or 4K(at least 3840×2160 pixels), thus resulting in a considerable increasein the image quality. The image recording frequency is usually 60 imagesper second in 4K and up to 240 images per second in full HD. Inaddition, an integrated image stabilizer may be provided. Action camerasoften also are equipped with an integrated microphone. Differentialsignal processing methods can be used to hide background noises in aspecific manner.

The image and sensor data 32 recorded by the camera and sensor device 30preferably are forwarded to the data analysis device 40 via a wirelessmobile radio connection.

The data analysis device 40 preferably has a processor 41 that processesthe image and sensor data 32 by means of a detection system 400. Theprocessor 41 or a further processor also is designed to control thecamera and image recording device 30. However, it is also conceivablefor the image and sensor data 32 to be stored initially in a memory unit50 or a software module 55 and to be processed by the data analysisdevice 40 at a later time. The data analysis device 40 and the processor41 may be integrated in the vehicle 20 or may be in the form of acloud-based solution with a cloud computing infrastructure that isconnected to the vehicle 20 via a mobile radio connection.

The data analysis device 40 can access one or more further databases 60.The database 60 may store, for example, classification parameters foranalyzing the recorded image and sensor data 32 or further images and/orcharacteristic variables. Furthermore, target variables and targetvalues that define a safety standard may be stored in the database 60. Auser interface 70 for inputting further data and for displaying thecalculation results created by the data analysis device 40 may also beprovided. In particular, the user interface 70 may be a display with atouchscreen.

In connection with the invention, a “processor” can be understood asmeaning, for example, a machine or an electronic circuit or a powerfulcomputer. A processor may be a main processor (Central Processing Unit,CPU), a microprocessor or a microcontroller, for example anapplication-specific integrated circuit or a digital signal processor,possibly in combination with a memory unit for storing programinstructions, etc. A processor may also be understood as meaning avirtualized processor, a virtual machine or a soft CPU. It may also be aprogrammable processor equipped with configuration steps for carryingout the method disclosed herein or may be configured with configurationsteps in such a manner that the programmable processor implements thefeatures of the disclosed method, of the component, of the modules or ofother aspects and/or partial aspects of the invention. Highly parallelcomputing units and powerful graphics modules also be provided.

In connection with this disclosure, a “memory unit” or “memory module”and the like may be understood as meaning, for example, a volatilememory in the form of a random access memory (RAM) or a permanent memorysuch as a hard disk or a data storage medium or, for example, aninterchangeable memory module. However, the memory module may also be acloud-based memory solution.

In connection with this disclosure, a “module” can be understood asmeaning, for example, a processor and/or a memory unit for storingprogram instructions. For example, the processor may specifically beconfigured to execute the program instructions such that the processorperforms functions to implement or execute the method according to thisdisclosure or a step of the method.

In connection with the invention, recorded image and sensor data 32should be understood as meaning both the raw data and alreadypreprocessed data from the recording results of the image and sensordevice 30.

In particular, the image and sensor device 30 may have mobile radiomodules of the 5G standard. 5G is the fifth generation mobile radiostandard and, in comparison with the 4G mobile radio standard, isdistinguished by higher data rates of up to 10 Gbit/sec, the use ofhigher frequency ranges, for example 2100, 2600 or 3600 megahertz, anincreased frequency capacity and therefore an increased data throughputand real-time data transmission, since up to one million devices persquare kilometer can be addressed at the same time. The latencies are afew milliseconds to less than 1 ms, with the result that real-timetransmissions of data and calculation results are possible. The imageand sensor data 32 recorded by the image and sensor device 30 aretransmitted in real time to a cloud computing platform where thecorresponding analysis and calculation are carried out. The analysis andcalculation results are transmitted back to the vehicle 20 in real timeand can therefore be quickly integrated in handling instructions for thedriver or in automated driving functions. This speed when transmittingthe data is necessary if cloud-based solutions are intended to be usedfor processing the image and sensor data 32. Cloud-based solutionsprovide the advantage of high and therefore fast computing powers.

If the data analysis device 40 is integrated in the vehicle, AI hardwareacceleration, such as the Coral Dev Board, is used for the processor 41to enable real-time processing. This is a microcomputer with a tensorprocessing unit (TPU), as a result of which a pre-trained softwareapplication can evaluate up to 70 images per second.

FIG. 2 shows the detection system 400 of the invention which in the formof a software application for analyzing and processing the capturedand/or stored image and sensor data 32 to detect a change in themovements of the vehicle body 12 in the case of the vehicle 10. Inparticular, the detection system 400 processes the captured image andsensor data 32 by means of artificial intelligence and machine imageanalysis algorithms in order to select and classify said data. Thedetection system 400 advantageously uses algorithms from the field ofmachine learning, preferably deep learning with convolutional neuralnetworks, for example, for analyzing the captured image and sensor data32. In addition, the image and sensor data 32 from the various sensorsources such as optics, acoustics and distance measurement can becombined with one another in order to obtain a comprehensive picture ofa driving situation.

A neural network comprises neurons arranged in layers and connected toone another differently. A neuron is able to receive information fromthe outside or from another neuron at its input, to assess theinformation in a particular manner and to forward the information to afurther neuron in a changed form at the neuron output or to output it asa final result. Hidden neurons are arranged between the input neuronsand output neurons. Depending on the network type, there may be aplurality of layers of hidden neurons. They ensure that the informationis forwarded and processed. Output neurons finally provide a result andoutput it to the outside world. Arranging and linking the neuronsproduces different types of neural networks such as feed-forwardnetworks, recurrent networks or convolutional neural networks. Thenetworks can be trained by means of unsupervised or monitored learning.

The detection system 400 has an analysis module 410 in the form of aconvolutional neural network (CNN). The image and sensor data 32 fromthe camera and sensor device 30 are used as input data of the analysismodule 410. Data from the database 60 also can be used. The data formatsof the input data are preferably in the form of tensors. In addition,different image formats can be used.

The convolutional neural network is a special form of an artificialneural network. It has a plurality of convolutional layers and is veryhighly suited to machine learning and applications with artificialintelligence (AI) in the field of image and voice recognition. Themethod of operation of a convolutional neural network is modeled to acertain extent on biological processes and the structure is comparableto the visual cortex of the brain. A convolutional neural network isusually trained in a monitored manner. Conventional neural networkscomprise fully meshed or partially meshed neurons in a plurality oflevels. However, these structures reach their limits when processingimages since a number of inputs corresponding to the number of pixelswould have to be available. The convolutional neural network is composedof different layers and, in terms of the basic principle, is a partiallylocally meshed neural feed-forward network. The individual layers of theCNN are the convolutional layer, the pooling layer and the fully meshedlayer. The pooling layer follows the convolutional layer and may bepresent multiple times in succession in this combination. Since thepooling layer and the convolutional layer are locally meshedsubnetworks, the number of connections in these layers remains limitedeven in the case of large input volumes and remains in a manageableframework. A fully meshed layer forms the termination. The convolutionallayer is the actual convolutional level and is able to detect andextract individual features in the input data. During image processing,these may be features such as lines, edges or particular shapes. Theinput data are processed in the form of tensors, such as a matrix orvectors. The pooling layer, also called subsampling layer, compressesand reduces the resolution of the detected features by appropriatefilter functions. In particular, a max pool function is used for thispurpose and calculates the maximum value for a (usually) non-overlappingportion of the data. However, mean value pooling can also be used inaddition to the maximum pooling. The pooling rejects superfluousinformation and reduces the volume of data. The performance duringmachine learning is not reduced as a result. The calculation speed isincreased as a result of the reduced volume of data.

The fully linked layer forms the termination of the convolutional neuralnetwork. It is the result of the repeated sequence of the convolutionaland pooling layers. All features and elements of the upstream layers arelinked to each output feature. The fully connected neurons may bearranged in a plurality of levels. The number of neurons is dependent onthe input data which are intended to be processed by the convolutionalneural network.

Therefore, in comparison with conventional non-convolutional neuralnetworks, the convolutional neural network (CNN) provides numerousadvantages. It is suitable for machine learning and artificialintelligence applications with large volumes of input data, such as inimage recognition. The network operates reliably and is insensitive todistortions or other optical changes. The CNN can process imagesrecorded in different light conditions and from different perspectives.It nevertheless detects the typical features of an image. Since the CNNis divided into a plurality of local, partially meshed layers, it has aconsiderably lower memory space requirement than fully meshed neuralnetworks. The convolutional layers drastically reduce the memoryrequirements. It likewise greatly shortens the training time of theconvolutional neural network. CNNs can be trained very efficiently usingmodern graphics processors. The CNN detects and extracts features of theinput images with the aid of filters. The CNN first of all detectssimple structures such as lines, color features or edges in the firstlayers. In the further layers, the convolutional neural network learnscombinations of these structures such as simple shapes or curves. Morecomplex structures can be identified with each level. The data areresampled and filtered again and again in the levels.

The image and sensor data 32 therefore preferably are processed by aconvolutional neural network in the analysis module 410. Aclassification module 430 that contains features M₁, M₂, . . . , M_(n)of movements of the vehicle body 12 of a vehicle also is provided. Inaddition, certain states S₁, S₂, . . . , S_(n) of the environment of thevehicle 10 may be assigned to these features M₁, M₂, . . . , M_(n).Certain fast upward and downward movements of the vehicle body 12 cantherefore indicate possible bumps and/or unevenness and damage of thesurface of the road 22. Lateral movements of the vehicle body 12 may bean indication of an obstacle on the road 22 and is evaded by the vehicle10 traveling in front. An excessively short distance to the vehicle 10traveling in front may be detected and may indicate a critical drivingsituation. Safety levels such as low to high may in turn be assigned tothe states S₁, S₂, . . . , S_(n). These features M₁, M₂, . . . , M_(n)and/or states S₁, S₂, . . . , S_(n) of the environment of the vehicle 10preferably were determined in a training phase by the CNN or werepredefined and transmitted to the classification module 430.

The image and sensor data 32 processed in this manner are integrated, asoutput data 450, in an automated driving function and/or are transmittedto the user interface 70. They may be output there as recommendedactions or warnings to the driver of the vehicle 20. For example, awarning tone or an optical indication, which is intended to prompt thedriver to adopt a changed driving behavior, may be output via the userinterface 70. In the case of an automated driving function, the drivingspeed may be automatically reduced, for example. Furthermore, provisionmay be made for the damping unit for the front axle and/or rear axle ofthe vehicle 20 to be automatically adapted, with the result that thedamping is set to be softer, for example, with the result that thevehicle 20 can drive over the bumps or road damage more safely and in amanner which is more comfortable for the occupants.

A method for detecting movements of the vehicle body of a motor vehicletraveling in front according to the present invention thereforecomprises the following steps:

In a step S10, image and sensor data 32 are recorded by a camera andsensor device 30 of a second motor vehicle 20. The image and sensor data32 represent the environment of the second motor vehicle 20 containingat least the first motor vehicle 10.

In a step S20, the image and sensor data 32 are forwarded to a dataanalysis device 40, wherein the data analysis device 40 comprises adetection system 400 for detecting movements of the vehicle body 12 ofthe first motor vehicle 10. The detection system uses algorithms fromthe field of artificial intelligence (AI) and machine image analysis.

In a step S30, the image and sensor data 32 are processed in the dataanalysis device 30 by the detection system 200 to classify possiblemovements of the vehicle body 12.

In a step S40, at least one state S_(j) from a set of defined states S₁,S₂, . . . , S_(n) is assigned to the classified movements.

In a step S50, output data 450 are generated from the determined stateS_(j) for further use in an automated driving function and/or for a userinterface 70.

Therefore, images from the environment of a second vehicle 20 can beanalyzed in real time with respect to the movements of the vehicle body12 in the case of a vehicle 10 traveling in front by means of adetection system 400 which uses algorithms from the field of artificialintelligence (AI) and machine image analysis. The present inventionmakes it possible to automatically capture movements of the vehicle body12 of the vehicle 10. The road situation, such as the occurrence ofbumps or damage to the road surface, can be derived from the classifiedmovements. The result of the analysis is output, for example, as anoptical and/or acoustic warning signal on a user interface 70 of thesecond vehicle 20 if the movements of the vehicle body 12 of the vehicle10 traveling in front indicate a critical driving situation for thesecond vehicle 20.

In addition, adaptations to the driving behavior or the setting ofvehicle components, such as the degree of damping for the front and/orrear axle, can be made by automatic or semi-automatic driving functions.If an excessively short distance to the vehicle 20 traveling in front isdetected, the driving speed of the vehicle 10 can be automaticallyreduced or a braking process can be initiated. Since the data aretransmitted and evaluated in real time, a fast response in the region ofmilliseconds is possible. This is highly important, in particular, inthe case of a high driving speed of the vehicle 10 since only in thismanner is it possible to ensure that automatic driving functions canreact appropriately to a current driving situation. The presentinvention therefore makes it possible to further increase safety duringdriving.

REFERENCE SIGNS

-   10 First motor vehicle-   12 Vehicle body-   20 Second motor vehicle-   22 Road-   30 Camera and sensor device-   32 Image and sensor data-   35 RGB camera-   37 Acoustic sensors-   39 LiDAR system, ultrasonic system, radar system-   40 Data analysis device-   41 Processor-   50 Memory unit-   55 Software module-   60 Database-   70 User interface-   100 System-   400 Detection system-   410 Analysis module-   430 Classification module-   450 Output data-   500 Computer program product-   550 Program code

What is claimed is:
 1. A method for detecting movements of a vehiclebody of a first motor vehicle, the method comprising: recording imageand sensor data by means of a camera and sensor device of a second motorvehicle, the image and sensor data representing a part of theenvironment of the second motor vehicle that contains at least the firstmotor vehicle; forwarding the image and sensor data to a data analysisdevice that comprises a detection system for detecting movements of thevehicle body of the first motor vehicle, the detection system usingartificial intelligence algorithms and machine image analysis;processing the image and sensor data in the detection system of the dataanalysis device to classify movements of the vehicle body; assigning theclassified movements of the vehicle body to at least one state from aset of defined states; generating output data from the determined statefor further use in an automated driving function and/or for a userinterface.
 2. The method of claim 1, wherein the image and sensor dataare processed and output data are generated in real time.
 3. The methodof claim 1, wherein the detection system comprises an analysis moduleand a classification module.
 4. The method of claim 3, wherein thedetection system uses deep learning with a neural network.
 5. The methodof claim 4, wherein the neural network is a convolutional neuralnetwork.
 6. The method of claim 3, wherein the classification modulecontains features of movements of the vehicle body of a vehicle thatwere determined or predefined in a training phase of the classificationmodule.
 7. The method of claim 5, wherein the image and sensor data aretransmitted to the data analysis device by a mobile radio connection. 8.The method of claim 1, wherein the image and sensor device comprises atleast one of an action camera, acoustic sensors, a LiDAR system, anultrasonic system and a radar system.
 9. A system for detectingmovements of vehicle body of a first motor vehicle, comprising an imageand sensor device of a second motor vehicle, wherein the image andsensor data represent apart of an environment of the second motorvehicle that contains at least the first motor vehicle; and a dataanalysis device that includes a detection system that uses artificialintelligence algorithms and machine image analysis for detectingmovements of the vehicle body of the first motor vehicle, and the dataanalysis device being configured to process the image and sensor data,to classify movements of the vehicle body, to assign at least one statefrom a set of defined states to the classified movements of the vehiclebody and to generate output data from the determined state for furtheruse in an automated driving function and/or for a user interface. 10.The system of claim 9, wherein the image and sensor data are processedand output data are generated in real time.
 11. The system of claim 9,wherein the detection system comprises an analysis module and aclassification module.
 12. The system of claim 11, wherein the detectionsystem uses deep learning with a neural network.
 13. The system of claim11, wherein the classification module contains features of movements ofthe vehicle body of the first vehicle that were determined or predefinedin a training phase of the classification module.
 14. The system ofclaim 9, wherein the image and sensor data are transmitted to the dataanalysis device by a mobile radio connection.
 15. The system of claim 9,wherein the image and sensor device comprises at least one of an actioncamera, acoustic sensors, a LiDAR system, an ultrasonic system and aradar system.
 16. A computer program product comprising an executableprogram code which is configured such that, during its execution, itcarries out the method as of claim 1.